Cancer incidence and associated death rates affecting American Indian (AI) communities are significantly higher than other races in the United States. More specifically, renal cell carcinoma (RCC) is the most common form of cancer in AI populations with diagnoses occurring at a younger age when compared to other races. Current sequencing datasets that are used for improving personalized medicine lacks information from AI cancer samples due to the lack of AI involvement in genomic and genetic research. Our ultimate goal for this project is to translate our findings to improve precision medicine for all populations, understanding the unique causes and driver mutations in AI cancer patients is a necessity. We hypothesize that functionally relevant and unique mutational signatures and molecular pathway disruption exist in the AI cancer samples and their identification will provide significant insights into cancer pathogenesis, progression, and will also help to improve diagnoses and treatment.